Complex Event Processing (CEP) is a set of methods that allow efficient knowledge extraction from massive data streams using complex and highly descriptive patterns. Numerous applications, such as online finance, healthcare monitoring and fraud detection use CEP technologies to capture critical alerts, potential threats, or vital notifications in real time. As of today, in many fields, patterns are manually defined by human experts. However, desired patterns often contain convoluted relations that are difficult for humans to detect, and human expertise is scarce in many domains. We present REDEEMER (REinforcement baseD cEp pattErn MinER), a novel reinforcement and active learning approach aimed at mining CEP patterns that allow expansion of the knowledge extracted while reducing the human effort required. This approach includes a novel policy gradient method for vast multivariate spaces and a new way to combine reinforcement and active learning for CEP rule learning while minimizing the number of labels needed for training. REDEEMER aims to enable CEP integration in domains that could not utilize it before. To the best of our knowledge, REDEEMER is the first system that suggests new CEP rules that were not observed beforehand, and is the first method aimed for increasing pattern knowledge in fields where experts do not possess sufficient information required for CEP tools. Our experiments on diverse data-sets demonstrate that REDEEMER is able to extend pattern knowledge while outperforming several state-of-the-art reinforcement learning methods for pattern mining.
翻译:复杂事件处理(CEP)是利用复杂和高度描述性模式从大量数据流中有效提取知识的一套方法,许多应用,例如在线金融、保健监测和欺诈检测,都利用CEP技术实时捕捉关键警报、潜在威胁或重要通知。从今天起,在许多领域,模式是由人类专家手工界定的。然而,理想模式往往包含人类难以探测的混乱关系,在许多领域,人的专门知识稀缺。我们提出了REDEEMER(REEMEAR)(EPR) (EP Basement base D CEp PattErn MinER),这是旨在开采CEP模式的新强化和积极学习方法,目的是扩大所获取的知识,同时减少人类工作所需的大量多变空间的新的政策梯度方法,以及一种将CEP规则学习的强化和积极学习结合起来的新方法,同时尽量减少培训所需的标签数量。REDEEMER(REDEMEER)是第一个提出新的CEP规则的系统,事先未予遵守,同时减少人类工作需要的人类努力。我们为REEF提供大量学习工具的实地经验,这是我们不断学习所需的方法。